The graphical representation of a finite element model (undirected graphs) imposes some constraints on the choice of storage techniques and data structures; first, the storage…
Abstract
The graphical representation of a finite element model (undirected graphs) imposes some constraints on the choice of storage techniques and data structures; first, the storage structure must deal efficiently with sparse matrices; second, the retrieval method of an edge, of a finite element model, around selected nodes must minimize the multiple occurrences of the same edge if plotting efficiency is to be achieved; and third, the insertion and extraction of edges in a data structure must be independent of the selected nodes identification scheme. This paper evaluates the relative merit of elementary storage methods and data structures in terms of the time and space costs required to satisfy the above constraints. The theoretical costs are derived and the experimental costs are evaluated and compared. Depending on the homogeneity of the degree of the nodes, a static data structure or a linked list data structure using listed or sectioned hashing techniques are shown to yield the minimum time and space costs.
The purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs).
Abstract
Purpose
The purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs).
Design/methodology/approach
The model contains a cost objective function, generalized precedence relationship constraints, activity duration and start time constraints, lag/lead time constraints, execution mode (EM) constraints, project duration constraints, working time unit assignment constraints and resource constraints. The mixed-integer nonlinear programming (MINLP) superstructure of discrete solutions covers time–cost–resource options related to various EMs for project activities as well as variants for production process implementation.
Findings
The proposed model provides the exact optimal output data for project management, such as network diagrams, Gantt charts, histograms and S-curves. In contrast to classic scheduling approaches, here the optimal project structure is obtained as a model-endogenous decision. The project planner is thus enabled to achieve optimization of the production process simultaneously with resource-constrained scheduling of activities in discrete time units and at a minimum total cost.
Practical implications
A set of application examples are addressed on an actual construction project to display the advantages of proposed model.
Originality/value
The unique value this paper contributes to the body of knowledge reflects through the proposed MINLP model, which is capable of performing the exact cost optimization of production process (where presence and number of activities including their mutual relations are dealt as feasible alternatives, meaning not as fixed parameters) simultaneously with the associated resource-constrained project scheduling, whereby that is achieved within a uniform procedure.
Details
Keywords
Xu Zhang, Mark Goh, Sijun Bai and Zonghan Wang
Risk response decisions (RRDs) are vital for project risk mitigation. Although past research has focused on RRDs for independent single projects, it has scarcely explored how to…
Abstract
Purpose
Risk response decisions (RRDs) are vital for project risk mitigation. Although past research has focused on RRDs for independent single projects, it has scarcely explored how to make RRDs for single projects in project portfolios (SPPPs). Consequently, this study aims to bridge the gap in extant literature by developing an integrated approach to select risk response strategies (RRSs) for SPPPs considering objective adjustments and project interdependencies (PIs).
Design/methodology/approach
An integrated quality function deployment (QFD) method was used throughout this study. More so, a balanced score card (BSC) and stratified-Z-numbers-full consistency method (SZFUCOM) was applied to identify SPPP success criteria (SP3SC) to determine their weights. In addition, a spherical fuzzy set-design structure matrix (SFDSM) was used to quantify the correlation between the risks and the relationship between the risks and the predecessor projects. Consequently, the relationships between the risks and SP3SC and RRSs were described by the spherical fuzzy set (SFS) and Z-numbers, respectively. Besides, the results are weaved into QFD to transform SP3SC into risks and then into RRSs, while a linear optimization model is used to obtain the optimal RRSs. Lastly, a construction project portfolio (PP) was used to test the veracity of the results to prove their validity.
Findings
The approach to RRDs for single projects is observed to be different from that of SPPPs. In addition, this study finds that project portfolio objective adjustments (PPOAs) and PIs have significant impacts on RRDs given that they influence the risk priorities of independent single projects and SPPPs. Moreover, the application of an integrated QFD effectively synthesized the results from the findings of this study, as well as enabled companies to determine robust RRSs. Finally, the consistency results of the SZFUCOM were better than those of the triangular fuzzy number-full consistency method.
Originality/value
The study innovatively explores the method of RRDs for SPPP, which has been ignored by past research. SP3SC highly compatible with PP success is determined. Z-numbers are first used to evaluate the effect of RRSs to enhance the robustness of RRDs. The study proposes a method of RRDs comprehensively considering PPOAs and PIs, which provides robust methodological guidance for SPPP managers to control risks.